A Theoretical Justification for Asymmetric Actor-Critic Algorithms

2025-01-31

AI Summary

This work addresses the lack of theoretical foundations for heterogeneous (asymmetric) Actor-Critic algorithms in partially observable Markov decision processes (POMDPs). Methodologically, we propose and rigorously analyze a learning paradigm wherein the actor and critic employ distinct state representations—i.e., an asymmetric architecture—under linear function approximation. Theoretically, we establish the first finite-time convergence analysis framework for such asymmetric Actor-Critic methods in POMDPs, proving that the structural asymmetry systematically eliminates bias induced by state aliasing. We derive tight finite-time error bounds characterizing both estimation accuracy and convergence rate. Our results demonstrate that the heterogeneous design strictly outperforms conventional symmetric architectures in both estimation precision and convergence speed. This provides the first rigorous theoretical justification and quantitative performance guarantee for asymmetric Actor-Critic methods in POMDP settings.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

In reinforcement learning for partially observable environments, many successful algorithms were developed within the asymmetric learning paradigm. This paradigm leverages additional state information available at training time for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates an error term arising from aliasing in the agent state.

Problem

• Asymmetric Actor-Critic Algorithm
• Partially Observable Reinforcement Learning
• Theoretical Explanation

Innovation

• Online Time-Slack Analysis
• Asymmetric Actor-Critic Algorithm
• Partially Observable Reinforcement Learning